Researchers and others have used computers to receive and automatically process speech in a variety of contexts. For example, computers have been programmed to receive a person's speech and transcribe the speech into an electronic document (i.e., speech-to-text). These applications of computerized speech analysis are known by the term “automatic speech recognition” (ASR). Current tools and approaches to ASR, however, lack accuracy and robustness in recognizing speech, whether spontaneous, instigated by a stimulus, or read from a script. ASR of spontaneous speech (unprepared conversational speech) is particularly challenging due to the individual speaker, and such challenges are sometimes exacerbated by the unexpected nature of a natural conversation. Other applications of computerized speech analysis are directed to analyzing speech and language characteristics independently of the content of the speech. For example, systems have been developed for automatic acoustic phonetic analysis aimed at describing the manner in which a person is speaking rather than what the person is saying. These analytic approaches have been used extensively in applications such as voice stress analysis, lie detection applications and clinical applications.
During the past decade neuropsychological testing has become very sophisticated. There are dozens of frequently used neuropsychological tests. Tests that are currently available are standardized, highly accurate, and possess a high degree of predictive accuracy. Neuropsychological testing is regarded as highly accurate (80-95%). These tests are properly administered by healthcare professionals. Tests such as verbal fluency rely on their own trained perceptions plus standardized test scores. These professionals lack sophisticated computerized tools to investigate either the content or the form of the patient's performance on the test at a deeper level.